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k0_2 = elu(d_bn0_2(conv2d(k0_1, df_dim*2, d_h=1, d_w =1, name='d_k02_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k02_prelu')
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k1_0 = maxpool2d(k0_2, k=2, padding='VALID')
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#k1_0 = elu(d_bn1_0(conv2d(k0_2, df_dim*2, d_h=2, d_w =2, name='d_k10_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k10_prelu')
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k1_1 = elu(d_bn1_1(conv2d(k1_0, df_dim*2, d_h=1, d_w =1, name='d_k11_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k11_prelu')
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k1_2 = elu(d_bn1_2(conv2d(k1_1, df_dim*4, d_h=1, d_w =1, name='d_k12_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k12_prelu')
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k2_0 = maxpool2d(k1_2, k=2, padding='VALID')
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#k2_0 = elu(d_bn2_0(conv2d(k1_2, df_dim*4, d_h=2, d_w =2, name='d_k20_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k20_prelu')
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k2_1 = elu(d_bn2_1(conv2d(k2_0, df_dim*3, d_h=1, d_w =1, name='d_k21_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k21_prelu')
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k2_2 = elu(d_bn2_2(conv2d(k2_1, df_dim*6, d_h=1, d_w =1, name='d_k22_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k22_prelu')
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k3_0 = maxpool2d(k2_2, k=2, padding='VALID')
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#k3_0 = elu(d_bn3_0(conv2d(k2_2, df_dim*6, d_h=2, d_w =2, name='d_k30_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k30_prelu')
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k3_1 = elu(d_bn3_1(conv2d(k3_0, df_dim*4, d_h=1, d_w =1, name='d_k31_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k31_prelu')
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k3_2 = elu(d_bn3_2(conv2d(k3_1, df_dim*8, d_h=1, d_w =1, name='d_k32_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k32_prelu')
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k4_0 = maxpool2d(k3_2, k=2, padding='VALID')
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#k4_0 = elu(d_bn4_0(conv2d(k3_2, df_dim*8, d_h=2, d_w =2, name='d_k40_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k40_prelu')
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k4_1 = elu(d_bn4_1(conv2d(k4_0, df_dim*5, d_h=1, d_w =1, name='d_k41_conv', reuse = is_reuse), train=is_training, reuse = is_reuse), name='d_k41_prelu')
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k4_2 = d_bn4_2(conv2d(k4_1, 320, d_h=1, d_w =1, name='d_k42_conv', reuse = is_reuse), train=is_training, reuse = is_reuse)
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k5 = tf.nn.avg_pool(k4_2, ksize = [1, s16, s16, 1], strides = [1,1,1,1],padding = 'VALID')
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k5 = tf.reshape(k5, [-1, 320])
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#if (is_training):
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# k5 = tf.nn.dropout(k5, keep_prob = 0.6)
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#k6_real = linear(k5, 1, 'd_k6_real_lin')
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k6_id = linear(k5, 1001, 'd_k6_id_lin')
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#k6_pose = linear(k5, pose_dim, 'd_k6_pose_lin')
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return k6_id, k5 #tf.nn.sigmoid(k6_real), k6_real, tf.nn.softmax(k6_id), k6_id, tf.nn.softmax(k6_pose), k6_pose, k5
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def main(_):
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gpu_options = tf.GPUOptions(visible_device_list ="0", allow_growth = True)
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with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)) as sess:
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#logits, end_points = mobilenet_v2_FR_sz224(tf.random_normal(shape=[2, 96, 96, 3]), is_reuse=False, is_training = False)
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_, logits = discriminator(tf.random_normal(shape=[2, 96, 96, 3]), is_reuse=False, is_training = False)
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t_vars = tf.trainable_variables()
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for var in t_vars:
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print(var.name)
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print(var.shape)
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tf.global_variables_initializer().run()
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#print(end_points.keys())
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#print(end_points['global_pool'].get_shape())
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print(logits.get_shape())
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startTime = time.time()
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for _ in range(100):
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sess.run(logits)
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print(time.time() - startTime)
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startTime = time.time()
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for _ in range(100):
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sess.run(logits)
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print(time.time() - startTime)
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startTime = time.time()
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for _ in range(100):
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sess.run(logits)
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print(time.time() - startTime)
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if __name__ == '__main__':
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tf.app.run()
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# <FILESEP>
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from functools import partial
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from typing import (
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Optional,
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Union,
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Callable,
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)
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from torchmetrics import Metric
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from TAGLAS.data import *
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from TAGLAS.datasets import *
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from TAGLAS.evaluation import *
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from TAGLAS.tasks import *
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DATASET_TO_CLASS_DICT = {
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"cora": Cora,
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"pubmed": Pubmed,
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"wikics": WikiCS,
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"arxiv": Arxiv,
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"fb15k237": FB15K237,
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"wn18rr": WN18RR,
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"hiv": partial(Chembl, name="hiv"),
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"pcba": partial(Chembl, name="pcba"),
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"bbbp": partial(Chembl, name="bbbp"),
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"bace": partial(Chembl, name="bace"),
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"toxcast": partial(Chembl, name="toxcast"),
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"esol": partial(Chembl, name="esol"),
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"freesolv": partial(Chembl, name="freesolv"),
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